Community Learning Analytics with Industry 4.0 and Wearable Sensor Data

Abstract

Learning analytics in formal learning contexts is often restricted to collect and analyze data from students following curricula through a learning management system. In informal learning, however, a deep understanding of learners and entities interacting with each other is needed. The practice of exploring these interactions is known as community learning analytics. Mobile devices, wearables and interconnected Industry 4.0 production machines equipped with a multitude of sensors collecting vast amounts of data are ideal candidates to capture the goals and activities of informal learning settings. What is missing is a methodological approach to collect, manage, analyze and exploit data coming from such an interconnected network of artifacts. In this paper, we present a concept and prototypical implementation of a framework that is able to gather, transform and visualize data coming from Industry 4.0 and wearable sensors and actuators. Our collaborative Web-based visual analytics platform is highly embeddable and extensible on various levels. Its open source availability fosters research on community learning analytics on a broad level.